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PaperclipPerfector  1yr ago (text post)   3096 thread views

Friday Fun Thread for April 19, 2024

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The Fussy Suitor Problem: A Deeper Lesson on Finding Love

Inspired by the Wellness Wednesday post post by @lagrangian, but mostly for Friday Fun, the fussy suitor problem (aka the secretary problem) has more to teach us about love than I initially realized.

The most common formulation of the problem deals with rank of potential suitors. After rejecting r suitors, you select the first suitor after r that is the highest ranking so far. Success is defined as choosing the suitor who would have been the highest ranking among the entire pool of suitors (size n). Most analyses focus on the probability of achieving this definition of success, denoted as P(r), which is straightforward to calculate. The “optimal” strategy converges on setting r = n/e (approximately 37% of n), resulting in a success rate of about 37%.

However, I always found this counterintuitive. Even with optimal play, you end up failing more than half the time.

In her book The Mathematics of Love Hanna Fry suggests, but does not demonstrate, that we can convert n to time, t. She also presents simulations where success is measured by quantile rather than absolute rank. For instance, if you end up with someone in the 95th percentile of compatibility, that might be considered a success. This shifts the optimal point to around 22% of t, with a success rate of 57%.

Still, I found this answer somewhat unsatisfying. It remains unclear how much less suitable it is to settle for the 95th percentile of compatibility. Additionally, I wondered if the calculation depends on the courtship process following a uniform geometric progression in time, although this assumption is common.

@lagrangian pointed out to me that the problem has a maximum expected value for payoff at r = sqrt(n), assuming uniform utility. While a more mathematically rigorous analysis exists, I decided to start by trying to build some intuition through simulation.

In this variant of we consider payoff in utilitons (u) rather than just quantile or rank information. For convenience, I assume there are 256 suitors.

The stopping point based on sqrt(n) grows much more slowly than the n/e case, so I don’t believe this significantly alters any qualitative conclusions. I’m pretty sure using the time domain here depends on the process and rate though.

I define P(miss) as the probability of missing out or accidentally exhausting the suitors, ultimately “settling” for the 256th suitor. In that case you met the one, but passed them up to settle for the last possible persion. Loss is defined as the difference in utility between the suitor selected by stopping at the best suitor encountered after r, and the utility that would have been gained by selecting the actual best suitor. Expected Shortfall (ES) is calculated at the 5th percentile.

I generate suitors from three underlying utility distributions:

  • Exponential: Represents scenarios where there are pairings that could significantly improve your life, but most people are unsuitable.
  • Normal: Assumes the suitor’s mutual utility is an average of reasonably well-behaved (mathematically) traits.
  • Uniform: Chosen because we know the optimal point.

For convenience, I’ve set the means to 0 and the standard deviation to 1. If you believe I should have set the medians of the distributions to 0, subtract log(2) utilitons from the mean(u) exponential result.

Running simulations until convergence with the expected P(r), we obtain the following results:


| gen_dist |    r    | P(r) | P(miss) | <u> | <loss> | sd_loss | ES_5 | max_loss |
|----------|---------|------|---------|-----|--------|---------|------|----------|
|   exp    |   n/e   | 37%  |   19%   | 2.9 |  2.2   |   2.5   | 7.8  |   14.1   |
|   exp    | sqrt(n) | 17%  |   3%    | 3.0 |  2.1   |   1.8   | 6.6  |   14.8   |
|----------|---------|------|---------|-----|--------|---------|------|----------|
|   norm   |   n/e   | 37%  |   19%   | 1.7 |  1.2   |   1.5   | 4.6  |   7.0    |
|   norm   | sqrt(n) | 18%  |   3%    | 2.0 |  0.8   |   0.8   | 3.3  |   6.3    |
|----------|---------|------|---------|-----|--------|---------|------|----------|
|   unif   |   n/e   | 37%  |   19%   | 1.1 |  0.6   |   1.0   | 3.2  |   3.5    |
|   unif   | sqrt(n) | 17%  |   3%    | 1.5 |  0.2   |   0.5   | 2.1  |   3.5    |

What was most surprising to me is that early stopping (r = sqrt(n)) yields better results for both expected utility and downside risk. Previously, I would have assumed that since the later stopping criterion (r = n/e) is more than twice as likely to select the best suitor, the expected shortfall would be lower. However, the opposite holds true. You are more than 6 times as likely to have to settle in this scenario, so even if suitability is highly skewed as in the exponential case, expected value is still in favor of the r=sqrt(n) case! This is a completely different result than the r=n/e I had long accepted as optimal. The effect is even far more extreme than even the quantile-time based result.

All cases yield a positive expectation value. Since we set the mean of the generating distributions to 0, this implies that on average having some dating experience before deciding is beneficial. Don’t expect your first millihookup to turn into a marriage, but also don’t wait forever.

I should probably note for low, but plausible n <= 7, sqrt(n) is larger than n/e, but the whole number of suitors mean the optimal r (+/-1) is still given in the standard tables.

One curious factoid, is that actuaries are an appreciable outlier in terms of having a the lowest likelihood of divorce. Do they possess insights about modeling love that the rest of us don’t? I’d be very interested if anyone has other probabilistic models of relationship success. What do they know that the rest of the life, physical, and social sciences don't? Or is it that they are just more disposed to finding a suitable "good" partner than the one.

Thanks for this!

One modification I wonder if you'd be willing to simulate for us: what if we don't strictly require stopping at n? You could formalize it to something like "given a strategy that on average samples n suitors when run it to its stopping point, what are the loss/etc?" p(miss) becomes 0 by definition.

That feels more like real life: if the goal is to stop dating by 35, but I happen to be dating Hitler when I hit 35, I'm probably gonna push it to 36.

I really hope I don't have to resort to necrophilia by the time I'm 36, but either way, I'm sure the coroner will cut me some slack.

We all share that same hope, if I might dare a post that suggests consensus.

Mods, twist his nuts

(I really had to use all my willpower not to put the mod hat on for this comment, be thankful)

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